Business Process Mining:
An Industrial Application
W.M.P. van der Aalst1, H.A. Reijers1, A.J.M.M. Weijters1, B.F. van Dongen1, A.K. Alves de Medeiros1, M. Song2,1, and H.M.W. Verbeek1
1 Department of Technology Management, Eindhoven University of Technology, P.O.
Box 513, NL-5600 MB, Eindhoven, The Netherlands.w.m.p.v.d.aalst@tm.tue.nl
2 Dept. of Industrial Engineering, Pohang University of Science and Technology, San
31 Hyoja-Dong, Nam-gu, Pohang, 790-784, South Korea.
Abstract. Contemporary information systems (e.g., WFM, ERP, CRM,
SCM, and B2B systems) record business events in so-called event logs.
Business process mining takes these logs to discover process, control, data, organizational, and social structures. Although many researchers are developing new and more powerful process mining techniques and software vendors are incorporating these in their software, few of the more advanced process mining techniques have been tested on real-life processes. This paper describes the application of process mining in one of the provincial offices of the Dutch national public works department, responsible for the construction and maintenance of the road and water infrastructure. Using a variety of process mining techniques, we analyzed the processing of invoices sent by the various subcontractors and suppli-ers from three different psuppli-erspectives: (1) the process psuppli-erspective, (2) the organizational perspective and (3) the case perspective. For this pur-pose, we used some of the tools developed in the context of the ProM framework. The goal of this paper is to demonstrate the applicability of process mining in general and our algorithms and tools in particular.
Key words: Process mining, social network analysis, workflow management, business process management, business process analysis, data mining, Petri nets.
1
Introduction
Today, many enterprise information systems store relevant events in some struc-tured form. For example, workflow management systems typically register the start and completion of activities [2]. ERP systems like SAP log all transactions, e.g., users filling out forms, changing documents, etc. Business-to-business (B2B) systems log the exchange of messages with other parties. Call center packages but also general-purpose CRM systems log interactions with customers. These examples show that many systems have some kind of event log often referred to as “history”, “audit trail”, “transaction log”, etc. [5, 8, 19, 39]. The event log typically contains information about events referring to anactivityand a case. The case (also named process instance) is the “thing” which is being handled, e.g., a customer order, a job application, an insurance claim, a building permit,
etc. The activity (also namedtask,operation,action, orwork-item) is some op-eration on the case. Typically, events have a timestamp indicating the time of occurrence. Moreover, when people are involved, event logs will characteristi-cally contain information on the person executing or initiating the event, i.e., theperformer.
Besides the availability of event logs there is an increased interest in monitor-ing business processes. On the one hand, new legislation such as the Sarbanes-Oxley (SOX) Act [38] and increased emphasis on corporate governance is forcing organizations to follow their business activities more closely [22]. On the other hand, there is a constant pressure to improve the performance and efficiency of business processes. This requires more fine-grained monitoring facilities as is illustrated by today’s buzzwords such as BAM (Business Activity Monitoring), BOM (Business Operations Management), and BPI (Business Process Intelli-gence). However, the functionality offered by tools such as Cognos and Busi-nessObjects is limited to simple performance indicators such as flow time and utilization. Unfortunately, most of these systems do not focus on causal and dy-namic dependencies in processes and organizations. One of the few commercial software tools adopting a more process-oriented view on monitoring is the ARIS Process Performance Monitor (ARIS PPM) [23].
Business process mining, orprocess miningfor short, aims at the automatic construction of models explaining the behavior observed in the event log. For example, based on some event log, one can construct a process model expressed in terms of a Petri net. Over the last couple of years many tools and tech-niques for process mining have been developed [1, 4, 5, 7, 8, 12, 21, 23, 30, 39, 44]. Although process mining is very promising, most of the techniques make assump-tions which do not hold in practical situaassump-tions. For example, some techniques assume that there is no noise and have difficulties dealing with exceptions. Other approaches are limited to processes having a particular structure. Therefore, it is important to confront existing tools and techniques with event logs taken from real-life applications. In this paper we describe a case study based on a log of the process of handling invoices in a provincial office of the Dutch national public works department. This office is one of twelve offices, employing about 1,000 civil servants. The office is responsible for the construction and mainte-nance of the road and water infrastructure in its province, and in order to do this it subcontracts work to various parties such as road construction compa-nies, cleaning compacompa-nies, and environmental bureaus. Also, the provincial office purchases services and products to support its construction, maintenance, and administrative activities. We have used an event log containing information on more than 14,000 invoices as a starting point for mining the process perspec-tive (How?), the organizational perspecperspec-tive (Who?), and the case perspecperspec-tive (What?). (These perspectives are presented in detail in Section 3.) The results are reported in this paper and demonstrate the applicability of our tools in an industrial setting.
The remainder of this paper is organized as follows. Section 2 briefly dis-cusses related work. Section 3 introduces the concept of business process mining
and Section 4 discusses the ProM framework used for the case study. Section 5 describes the case study. Sections 6, 7, and 8 present the results of mining the process, organizational, and case perspective of the invoice handling process. Section 9 reflects on these results and concludes the paper.
2
Related work
In our case study we analyze the event log generated by the Workflow Manage-ment System (WfMS) of the organization involved. Clearly, most of the workflow literature [2, 24, 28] has been focusing on modeling, verification, simulation, and enactment rather than process mining. The idea of applying process mining in the context of workflow management was first introduced in [8]. Cook and Wolf have investigated similar issues in the context of software engineering processes using different approaches [12]. Herbst and Karagiannis also address the issue of process mining in the context of workflow management using an inductive ap-proach [21]. They use stochastic task graphs as an intermediate representation and generate a workflow model described in the ADONIS modeling language. Theαalgorithm [7] can be seen as the first algorithm to truly capture concur-rency in business processes. This algorithm was proven to be correct for a large class of processes [7], but like most other techniques it has problems in dealing with noise and incompleteness. Therefore, we developed the heuristic approach used in this paper [43, 44].
The focus of this paper is not limited to the control-flow perspective. In the case study we also investigate the organizational perspective. This work uses Social Network Analysis (SNA) [40, 42] techniques and tools. SNA can be seen as part of sociometry, the roots of which can be found in the early work of Moreno [29]. Although SNA has been around for a long time, recently its ap-plication increased because of the availability and widespread use of electronic communication and information facilities. For example, several studies have gen-erated sociograms from email logs [15–17, 31, 33] to analyze the communication structure inside or between organizations. Such studies have resulted in the iden-tification of relevant, recurrent aspects of interaction in organizational contexts [10, 17]. However, these studies are unable to relate the derived social networks to a particular business process, as the analyzed data does not reveal to what activity or case it applies. This paper builds on the results presented in [3, 4] where SNA is related to process mining.
As indicated in the introduction, business process mining can be seen in the broader context of Business (Process) Intelligence (BPI) and Business Activity Monitoring (BAM). In [18, 19, 39] a BPI toolset on top of HP’s Process Manager is described. The BPI toolset includes a so-called “BPI Process Mining Engine”. In [30] Zur M¨uhlen describes the PISA tool which can be used to extract perfor-mance metrics from workflow logs. Similar diagnostics are provided by the ARIS Process Performance Manager (PPM) [23]. The latter tool is commercially avail-able and a customized version of PPM is the Staffware Process Monitor (SPM) [41] which is tailored towards mining Staffware logs.
For more information on process mining we refer to a special issue of Com-puters in Industry on process mining [6] and a survey paper [5]. Given the scope of this paper, we are unable to provide a complete listing of the many papers on process mining published in recent years. However, in the next section we give a brief overview of the field.
3
Business Process Mining: An Overview
The goal of process mining is to extract information about processes from trans-action logs [5]. We assume that it is possible to record events such that (i) each event refers to anactivity(i.e., a well-defined step in the process), (ii) each event refers to a case (i.e., a process instance), (iii) each event can have aperformer also referred to as originator (the person executing or initiating the activity), and (iv) events have atimestampand are totally ordered. Table 1 shows an ex-ample of a log involving 19 events, 5 activities, and 6 originators. In addition to the information shown in this table, some event logs contain more information on the case itself, i.e., data elements referring to properties of the case. For ex-ample, the case handling system FLOWer [34] logs every modification of every data element.
Table 1.An event log.
case id activity id originator timestamp
case 1 activity A John 9-3-2004:15.01
case 2 activity A John 9-3-2004:15.12
case 3 activity A Sue 9-3-2004:16.03
case 3 activity B Carol 9-3-2004:16.07
case 1 activity B Mike 9-3-2004:18.25
case 1 activity C John 10-3-2004:9.23
case 2 activity C Mike 10-3-2004:10.34
case 4 activity A Sue 10-3-2004:10.35
case 2 activity B John 10-3-2004:12.34
case 2 activity D Pete 10-3-2004:12.50
case 5 activity A Sue 10-3-2004:13.05
case 4 activity C Carol 11-3-2004:10.12
case 1 activity D Pete 11-3-2004:10.14
case 3 activity C Sue 11-3-2004:10.44
case 3 activity D Pete 11-3-2004:11.03
case 4 activity B Sue 14-3-2004:11.18
case 5 activity E Clare 17-3-2004:12.22
case 5 activity D Clare 18-3-2004:14.34
case 4 activity D Pete 19-3-2004:15.56
Event logs such as the one shown in Table 1 are used as the starting point for mining. We distinguish three different perspectives: (1) the process
perspec-tive (“How?”), (2) the organizational perspecperspec-tive (“Who?”) and (3) the case perspective (“What?”).
Theprocess perspective focuses on the control-flow, i.e., the ordering of ac-tivities. The goal of mining this perspective is to find a good characterization of all possible paths, e.g., expressed in terms of a Petri net [36] or Event-driven Process Chain (EPC) [26].
Theorganizational perspectivefocuses on the originator field, i.e., which per-formers are involved and how are they related. The goal is to either structure the organization by classifying people in terms of roles and organizational units or to show relations between individual performers (i.e., build a social network [29, 32, 40, 42]).
Thecase perspectivefocuses on properties of cases. Cases can be characterized by their path in the process or by the originators working on a case. However, cases can also be characterized by the values of the corresponding data elements. For example, if a case represents a replenishment order, it may be interesting to know the supplier or the number of products ordered.
A
B
C
D E
(a) The control-flow structure expressed in terms of a Petri net.
(b) The organizational structure expressed in terms of a activity-role-performer diagram.
John Sue Mike Carol Pete Clare role X role Y role Z
John Sue
Mike
Carol Pete
Clare
(c) A sociogram based on transfer of work.
Fig. 1.Some mining results for the process perspective (a) and organizational (b and c) perspective based on the event log shown in Table 1.
To illustrate the first two perspectives consider Figure 1. The log shown in Table 1 contains information about five cases (i.e., process instances). The log
shows that for four cases (1, 2, 3, and 4) the activities A, B, C, and D have been executed. For the fifth case only three activities are executed: activities A, E, and D. Each case starts with the execution of A and ends with the execution of D. If activity B is executed, then also activity C is executed. However, for some cases activity C is executed before activity B. Based on the information shown in Table 1 and by making some assumptions about the completeness of the log (i.e., assuming that the cases are representative and a sufficient large subset of possible behaviors is observed), we can deduce the process model shown in Figure 1(a). The model is represented in terms of a Petri net [36]. The Petri net starts with activity A and finishes with activity D. These activities are represented by transitions. After executing A there is a choice between either executing B and C in parallel or just executing activity E. Note that for this example we assume that two activities are in parallel if they appear in any order. By distinguishing between start events and complete events for activities it is possible to explicitly detect true parallelism, i.e., concurrent execution of tasks. Figure 1(a) does not show any information about the organization, i.e., it does not use any information on the people executing activities. However, Ta-ble 1 shows information about the performers. For example, we can deduce that activity A is executed by either John or Sue, activity B is executed by John, Sue, Mike or Carol, C is executed by John, Sue, Mike or Carol, D is executed by Pete or Clare, and E is executed by Clare. We could indicate this information in Figure 1(a). The information could also be used to “guess” or “discover” organi-zational structures. For example, a guess could be that there are three roles: X, Y, and Z. For the execution of A role X is required and John and Sue have this role. For the execution of B and C role Y is required and John, Sue, Mike and Carol have this role. For the execution of D and E role Z is required and Pete and Clare have this role. For five cases these choices may seem arbitrary but for larger data sets such inferences capture the dominant roles in an organization. The resulting “activity-role-performer diagram” is shown in Figure 1(b). The three “discovered” roles link activities to performers. Figure 1(c) shows another view on the organization based on the transfer of work from one individual to an-other, i.e., not focusing on the relation between the process and individuals but on relations among individuals (or groups of individuals). Consider for example Table 1. Although Carol and Mike can execute the same activities (B and C), Mike is always working with John (cases 1 and 2) and Carol is always working with Sue (cases 3 and 4). Probably Carol and Mike have the same role but based on the small sample shown in Table 1 it seems that John is not working with Carol and Sue is not working with Mike.1 These examples show that the event log can be used to derive relations between performers of activities, thus result-ing in a sociogram. For example, it is possible to generate a sociogram based on the transfers of work from one individual to another as is shown in Figure 1(c). Each node represents one of the six performers and each arc represents that there
1 Clearly the number of events in Table 1 is too small to establish these assumptions
accurately. However, for the sake of argument we assume that the things that did not happen will never happen.
has been a transfer of work from one individual to another. There is a “transfer of work from A to B” if, for the same case, an activity executed by A is directly followed by an activity executed by B. For example, both in case 1 and 2 there is a transfer from John to Mike. Figure 1(c) does not show frequencies. However, for analysis purposes these frequencies can be added. The arc from John to Mike would then have weight 2. (Typically, we do not use absolute frequencies but weighted frequencies to get relative values between 0 and 1.) Figure 1(c) shows that work is transferred to Pete but not vice versa. Mike only interacts with John and Carol only interacts with Sue. Clare is the only person transferring work to herself.
Besides the “How?” and “Who?” question (i.e., the process and organization perspectives), there is the case perspective that is concerned with the “What?” question. Figure 1 does not address this. In fact, focusing on the case perspec-tive is most interesting when also data elements are logged but these are not listed in Table 1. The case perspective looks at the case as a whole and tries to establish relations between the various properties of a case. Note that some of the properties may refer to the activities being executed, the performers working on the case, and the values of various data elements linked to the case. Using clustering algorithms it would for example be possible to show a positive corre-lation between the size of an order or its handling time and the involvement of specific people.
Orthogonal to the three perspectives (process, organization, and case), the result of a mining effort may refer tological issues and/or performance issues. For example, process mining can focus on the logical structure of the process model (e.g., the Petri net shown in Figure 1(a)) or on performance issues such as flow time. For mining the organizational perspectives, the emphasis can be on the roles or the social network (cf. Figure 1(b) and (c)) or on the utilization of performers or execution frequencies.
4
ProM: A Process Mining Framework
As indicated in the introduction the functionality of commercial tools is typi-cally limited to measuring and analyzing performance indicators such as flow times, failure rates, and frequencies. As shown in the previous section, process mining involves the construction of models and is not limited to simple metrics. Although buzzwords such as BAM (Business Activity Monitoring), BOM (Busi-ness Operations Management), and BPI (Busi(Busi-ness Process Intelligence) suggest differently, commercial systems are typically unable to discover non-trivial mod-els. In the last 5 years, several mining tools have been developed at Eindhoven University of Technology, e.g., EMiT [1], Thumb [44], and MiSoN [4]. These tools refer to different perspectives and use different mining techniques. How-ever, they work on the same type of event logs and may create similar types of models. Therefore, these tools have been integrated in the ProM framework [14].
Fig. 2. Screenshot of ProM showing two plug-ins applied to the event log shown in Table 1.
The ProM framework has been developed as a completely plug-able environ-ment. It can be extended by simply adding plug-ins, i.e., there is no need to know or recompile the source code. Currently, more than 90 plug-ins have been added. The most interesting plug-ins are the mining plug-ins and the analysis plug-ins. The architecture of ProM allows for five different types of plug-ins: Mining plug-ins which implement some mining algorithm, e.g., mining
algo-rithms that construct a Petri net based on some event log.
Export plug-ins which implement some “save as” functionality for some ob-jects (such as graphs). For example, there are plug-ins to save EPCs, Petri nets, spreadsheets, etc.
Import plug-ins which implement an “open” functionality for exported ob-jects, e.g., load instance-EPCs from ARIS PPM.
Analysis plug-ins which typically implement some property analysis on some mining result. For example, for Petri nets there is a plug-in which constructs place invariants, transition invariants, and a coverability graph.
Conversion plug-ins which implement conversions between different data for-mats, e.g., from EPCs to Petri nets.
Earlier tools such as EMiT [1], Thumb [44], and MiSoN [4] have been refactored as plug-ins in the ProM framework. Figure 2 shows a screenshot of ProM. Note that one plug-in shows the discovered process model in terms of a Petri net. This Petri net is identical to the one shown in Figure 1(a). The other plug-in shows the sociogram also depicted in Figure 1(c). Both models have been constructed automatically from the event log shown in Table 1.
Fig. 3.The MXML format for process mining (XML Schema).
The ProMimportcan be used to import event logs from various systems (e.g., Staffware and FLOWer) such that they can be analyzed using ProM. ProM uses a standard XML format, namedMXML[13]. In our case study the proprietary format of the workflow management system used by provincial office was mapped onto the XML format. Therefore, we discuss the format in some more detail. Understanding the format is also important for understanding the applicability of ProM.
Figure 3 illustrates the standard XML format. TheSourceelement contains the information about software or system that was used to record the log. The Process element represents one process holding multiple cases. The ProcessIn-stance elements correspond to cases. One ProcessInstance element may hold multipleAuditTrailEntryelements. Each of these elements represents an event, i.e., one line in a table like Table 1. EachAuditTrailEntryelement may contain WorkflowModelElement, EventType,Timestamp, andOriginatorelements. The WorkflowModelElementandEventTypeare mandatory elements as shown in Fig-ure 3. The WorkflowModelElement element refers to an activity, a subprocess, or some other routing element in the process model. TheEventTypeelement can be used to record the type of event (e.g., the start or completion of an activity or some exceptional behavior like the cancellation of a case). Table 1 does not show any event types. However, one can always use the default event type com-plete. TheTimestampelement can be used to record the time of occurrence. The Originatorelement refers to the performer, e.g., the person executing the corre-sponding activity. To make the format more expressive, we defineDataelement that can be used at various levels (i.e.,WorkflowLog,Process, ProcessInstance,
andAuditTrailEntrylevel). If users want to specify additional information, this can be recorded using theDataelement (e.g., data elements linked to cases).
In the subsequent sections we will provide more information on the mining tools we are using in this case study but first we provide more information on the case study itself.
5
The Public Works Department
The industrial application in this paper involves one of the twelve provincial offices of the Dutch national public works department. In the Netherlands this department is referred to as “Rijkswaterstaat”, abbreviated as “RWS”. Like all other RWS offices, the particular office studied here is primarily responsible for the construction and maintenance of the road and water infrastructure in its province. About 1,000 civil servants work for this office. To perform its functions, the RWS office subcontracts various parties such as road construction companies, cleaning companies, and environmental bureaus. Also, it purchases services and products to support its construction and maintenance activities on the one hand (e.g., mechanical tools, fuel, and rasters) and its administrative activities on the other (e.g., office supplies). In 2001, the RWS office processed 20,000 invoices from various subcontractors and suppliers.
Before 2001, the twelve provincial offices maintained 18 different process versions to handle the various invoices. This diversity made it difficult and time-consuming to update all local payment processes to adhere to changing national regulations. In addition, the performance of all these different process versions varied. An important performance indicator for the processing of invoices is the timeliness of payment. For legitimate invoices holds that payment should take place within 31 days from the moment the invoice was received. After this period, the creditor is entitled (on the basis of Dutch law) to receive interest over the outstanding sum. Clearly, a slack payment attitude negatively influences the financial position of an RWS office. For the office studied in this particular case, it became clear that the norms for payment timeliness were not met (see Table 2). As shown, the norm states that 90% or more of the invoices must be payed before the appointed time of 31 days, a maximum of 5% should be paid within 31 to 62 days, and again up to a maximum of 5% may be paid after 62 days. In the second column, the actual performance of the RWS office in question is given.
Table 2.The time until invoices are paid (i.e., norms and actual performance before the implementation of a workflow management system).
payment duration (days) Norm Actual
0−31 90% 70%
32−62 5% 22%
In response to these issues, the national RWS management decided to unify invoice processing across the various provincial offices in search of efficiency gains. The momentum of change was seized to develop a proprietary workflow management system to support and further optimize the processing of invoices. One of the main promises of workflow technology is that it supposedly speeds up the processing, by liberating workers from routine work they need for coordi-nation and by handing out work to exactly the right resources at the right time [27]. In 2002, the workflow management system was implemented at the RWS office involved in our case study.
The contact with the RWS office was established in 2001, when Eindhoven University of Technology in a joint effort with Deloitte management consultancy initiated a longitudinal study into the effectiveness of workflow management sys-tems. The aim of the study - which is still running - is to quantify the contri-bution of workflow technology to improved business process performance with respect to lead time, wait time, service time, and utilization of resources. More information on this study including its preliminary results can be found in [35]. The RWS office expressed its interest in the mentioned study and partici-pated as one of the 10 Dutch organizations where workflow management effec-tiveness would be measured. During the years 2001 and 2002, information was gathered for comparison purposes on the performance of the invoice processing, both before and after the implementation of the workflow management system. The data which is analyzed and mined in this paper involves the situationafter which the workflow management system was implemented. This data was gath-ered after the system had been running for a number of months (to avoid any startup effects). The management of the provincial office supported this analysis and was interested to see how mining techniques could contribute to a better understanding of the performance of the process and perhaps identify opportu-nities for improvement. Therefore, RWS provided us with the logs of the invoice payment process.
The invoice process analyzed in this paper consists of 17 real activities, aside from logistic steps and splits. The RWS event log (or ‘RWS log’ for short) con-tains 14,279 cases. The total number of logged events is 147,579 and 487 employ-ees participated in the process execution. Figure 4 shows a snippet of the RWS log. The left-hand side shows the native format of the workflow management system used by the RWS office. The right-hand side shows the same log in the MXML format described in Section 4.
6
Mining the Process Perspective
As indicated before, we will analyze the RWS log from three perspectives. In this section, we focus on theprocess perspective, also known as thecontrol-flow perspective. Before presenting the results of applying process mining in the case study, we first need to tell more about the particular process mining technique being used.
Fig. 4. A snippet of the RWS log in its native format (left) and the MXML format (right).
For the process perspective, we only consider the case and activity attributes of an event log, e.g., in Table 1 we only need to consider the first two columns. To construct a process model like in Figure 1(a), we need to be able to discover causal dependencies and decide on the types of splits and joins.A→W Bdenotes the casual dependency between activitiesAandB, i.e., Ais (directly) followed byB butB is not (directly) followed byA. This is indeed the case in Table 1. Moreover, A→W C, A →W E, B →W D, C →W D, and E →W D. For this simple example it is easy to discover the causal dependencies, however, for more realistic logs (such as the RWS log) there are two complicating factors:
– Completeness
For larger or more complicated processes the log will typically not contain all possible routes. Consider 10 activities which can be executed in parallel. The total number of interleavings is 10! = 3,628,800. It is not realistic that each interleaving is present in the log. Moreover, certain paths through the process model may have a low probability and therefore remain undetected. As a result the log is not complete in the sense that it does not capture all possible behavior.
– Noise
Parts of the log may be incorrect, incomplete, or refer to exceptions. Events can be logged incorrectly because of human or technical errors. Events can be missing in the log if some of the activities are manual or handled by another system/organizational unit. Events can also refer to rare or undesired events.
Clearly, exceptions which are recorded only once should not automatically become part of the regular process model.
To tackle these problems we have chosen to use theheuristic approach de-scribed in [43, 44]. This approach is relatively robust (i.e., it can deal with noise and incompleteness) and has options to focus on the main process instead of trying to model the full details of the behavior reported in the event log. For a better understanding of the approach we shortly discuss the ideas to discover causal dependencies in the presence of noise.
We use a frequency based metric to indicate how certain we are that there is a dependency relation between two activities A and B (notationA⇒W B). The basic idea is that if activity A is often directly followed by activity B, but the opposite (B directly followed by A) never occurs, then there is a high probability that there is a dependency relation between A and B. Below, we first define the
⇒W metric. After that we will illustrate how we can use this metric in a simple heuristic in which we search for reliable dependency relations (the A →W B relation).
LetT be a set of activities,W be an event log overT, anda, b∈T : – |a >W b| is the number of times a >W b occurs in W (i.e., the number of
times eventais directly followed by eventb),
– a⇒W b=
µ
|a >W b| − |b >W a|
|a >W b|+|b >W a|+ 1
¶
First, note that the value of a ⇒W b is always between -1 and 1. Some simple examples demonstrate the rationale behind this definition. If we use this definition in the situation that, in 5 cases, activity A is directly followed by activity B but the other way around never occurs, the value ofA⇒W B= 5/6 = 0.833 indicating that we are not completely sure of the dependency relation. After all, there are only 5 observations and these may correspond to noise. However, if there are 50 cases in the event log in which A is directly followed by B but the other way around never occurs, the value ofA⇒W B= 50/51 = 0.980 indicates that we are more confident about the causality relation. If there are 50 traces in which activity A is directly followed by B and noise caused B to follow A once, the value of A ⇒W B is 49/52 = 0.94 indicating that we are pretty sure of a causal relation.
A highA⇒W B value strongly suggests that there is a causal relation be-tween activities A and B. But what is a high value? What would be a good threshold to take the decision that B truly depends on A (i.e.,A→W B holds)? Such a threshold appears sensitive for the amount of noise, the degree of con-currency in the underlying process, and the frequency of the involved activities. On closer inspection, it appears unnecessary to use a threshold value. After all, we know that each non-initial activity must have at least one other activity that is its cause, and each non-final activity must have at least one dependent activity. Using this information in a heuristic approach we can limit the search
and take the best candidate (with the highest A ⇒W B score)2. This simple heuristic helps us enormously in finding reliable causality relations even if the event log contains noise.
Although the heuristic formulated above is not complete and has to be ex-tended to recognize (i) recursion, (ii) short loops, and (iii) the type of joins and splits (i.e., AND or XOR) we can now understand the meaning of a statement like “A ⇒W B = 0.98” (i.e., the dependency value between activity A and B calculated equals 0.98).
Applying the approach described above (with default parameter settings) to the RWS log results in the dependency graph of Figure 5. Each node in the dependency graph represents an activity. Note that the activity names are in Dutch. Since these are the names that appear in the log, we cannot change them without changing the entire log. (Figure 5 is generated automatically on the basis of the event log containing 147,579 events.) The arcs in the graph represent the causal dependencies. D:expRWSRWS1DF.dot bBb 14279 020_Contr_betstuk 14280 1.000|14279| 030_1e_Vastlegging 15660 1.000|14029| 210_Afvoeren_betstuk 1236 0.963|185| 050_Adm_akkoord 20021 0.998|15086| 170_Parkeer 3618 0.958|107| eEe 14279 0.975|194| 0.926|272| 0.998|1516| 070_PV 16598 0.999|14014| 0.998|1405| 0.979|237| 080_Contract_akkoord 4221 0.987|2290| 0.833|370| 160_Wachten_op_VPL 189 0.975|189| 0.999|2973| 180_Verificatie 13589 1.000|11132| 0.994|729| 0.968|152| 0.996|747| 110_Afhandelen_afw 1439 0.994|865| 0.978|195| 150_2e_Vastlegging 13316 1.000|13316| 0.915|71| 0.958|107| 0.998|1391| 0.994|725| 0.978|191| 0.997|1167| 130_Aanpassen_code 1236 0.995|1015| 220_Afsluiten 13472 1.000|12293| 0.995|943| 0.956|287| 1.000|13472| 0.987|365| 0.996|1859| 0.991|569| 0.997|1182| 0.969|156| 190_Wachten_op_PV 17 0.783|17| 0.915|654| 0.956|221| 0.914|409| 200_Wachten_op_CF 129 0.861|129| 0.833|17| 0.996|1179| 0.914|31| 0.975|189| 0.952|99| 0.861|29| 0.783|17|
Fig. 5.The resulting dependency graph of applying the mining algorithm with default parameter settings.
ActivitiesbBbandeEein Figure 5 are artificially addedbeginandend activ-ities. Adding the extra end activityeEemakes it clear that not all instances end with the intended end activity220 Afsluiten; also, activities030 1e Vastlegging, 070 PV, and050 Adm akkoord appear as end-activities. The number in the ac-tivity box indicates the frequency of that acac-tivity (e.g., the frequency of the first
2 Or the best candidate plus all candidates with aA⇒
W Bscore close (default 95%)
real activity020 Contr betstukis 14,280). The string 1.000|14029|close to the arrow from activity020 Contr betstukto activity030 1e Vastleggingmeans that in the RWS log there are 14,029 registrations of activity020 Contr betstuk directly followed by activity030 1e Vastlegging; for this reason the calculated dependency value between this two activities is very high (i.e., 1.000).
A close observation of Figure 5 reveals that there are many loops in the model, specifically around the activity 170 Parkeer. The way this activity is connected with other activities indicates the special status of this activity; after some discussion with the owners of the process it appears that170 Parkeer is not really an activity but a way to suspend the processing of the case temporar-ily. With respect to the other loops, many of these reflect how cases are at times classified incorrectly at the start of the process, which then after some time re-quires a re-evaluation of the case. For example, the case is routed to a department supposedly managing the involved contract. Furthermore, this process involves a number of checks (e.g., activity180 Verificatie,080 Contract akkoord) that lead to re-iterations in case the quality of processing is not satisfactory.
After discussing the various issues with the process owners of the RWS the decision was taken to concentrate on the main process and to ignore the suspension facility 170 Parkeer and all low frequent activities (i.e., activities with a frequency below 1% of the total number of events (147,579)). This re-sults in ignoring the following 6 activities: 190 Wachten op PV(frequency=17), 200 Wachten op CF (129), 160 Wachten op VPL (189), 210 Afvoeren betstuk (1,226),130 Aanpassen code(1,236), and110 Afhandelen afw(1,439). The de-pendency graph resulting from this abstraction is presented in Figure 6.
Figure 6 gives a good impression of the main flow (the vertical axis of the graph) in the RWS-process. The relatively high values between vertical bars (i.e., the “direct followed by” measure) indicate that the process has a strong sequential character with some alternative paths and some loops. During the mining of the process also the type of each split and join is discovered. For instance, the first split (i.e., after020 Contr betstuk) is an XOR-split and the last join (i.e., before 220 Afsluiten) is an XOR-join. This information is not shown in Figure 6 but is present and can be used to generate a process model in terms of a Petri net or EPC. Using the information on splits and joins we can easily check the quality of the model by trying to parse the material in the log. An incomplete model (i.e., a model with missing causal dependencies) or errors in the type of splits and joins will result in parsing errors. The mined model partly presented in Figure 6 (the AND/XOR information is not presented in the figure) is able to parse 13,465 of the 14,279 cases completely correct. With 814 cases there are parsing problems (i.e., 812 of them leave some enabled activities and in 101 cases there are activities that can not be parsed). Note that the total sums up to a higher value than 814 because a case can have more than one parsing problem.
From the perspective of RWS, the mining analysis delivered a highly infor-mative process model. In comparison with the predefined process model that the workflow management system uses to operate, the mined model clearly shows
D:expRWSplaatje41.dot bBb 14279 020_Contr_betstuk 14280 1.000|14279| 030_1e_Vastlegging 15660 1.000|14029| 220_Afsluiten 13472 0.974|184| 0.958|115| 050_Adm_akkoord 20021 0.998|15086| 0.981|264| eEe 14279 0.975|194| 0.998|1516| 0.997|1597| 070_PV 16598 0.999|14014| 0.986|353| 0.980|251| 080_Contract_akkoord 4221 0.987|2290| 0.999|2973| 0.993|725| 180_Verificatie 13589 1.000|11798| 0.973|179| 0.972|176| 0.996|747| 0.981|253| 150_2e_Vastlegging 13316 0.997|13316| 0.997|902| 1.000|12401| 1.000|13472| 0.987|365| 0.996|1859| 0.992|635| 0.945|86| 0.948|92| 0.996|1184|
Fig. 6. The resulting dependency graph after ignoring the artificial activity
the same main flow of invoices being handled. In this way, infrequently executed activities can be left out for a better understanding of the process. Further-more, the mined model indicates that many cases will go through loops during their life-cycle. This in particular cannot be deduced from the predefined process model which, by its very nature, lacks information on actual behavior.
In the remainder, we focus on the organizational perspective and the case perspective. We show how we used additional mining analyses to provide more insight into the nature of the loops shown in Figure 6 and their effect on the performance of the process.
7
Mining the Organizational Perspective
In this section, we examine the organizational perspective. In other words, we focus on “who” performs the different steps and how performers are related. One of the basic ideas is that relationships between workers may be derived from the frequency of passing a case from one performer to another. For analyzing such relationships, the tool MiSoN has been developed [4]. The functionality of MiSoN has been embedded in the ProM framework (see Figure 2 for a screenshot). Based on the event logs extracted from these systems, our tool constructs sociograms that can be used as a starting point for social network analysis (SNA) [40, 42]. The derived relationships can be exported in a matrix format and used by most SNA tools, such as AGNA and NetMiner. With an SNA tool, several techniques can be applied to analyze social networks, e.g., find interaction patterns, evaluate the role of an individual in an organization, etc.
When we consider the RWS log, an obvious way to start is to look for direct hand-overs of work within cases between performers (see the discussion of Fig-ure 1(c) in Section 3). From an analysis of the RWS log, we can derive a social network as shown in Figure 7. A directed arc betweenuser32anduser28, for ex-ample, represents that on some occasion a case was handed over fromuser32 to user28.3It can easily be verified that the presented network contains no isolated nodes.
Note that the represented network contains only 43 nodes, all representing human users, while the original RWS log contains 487 performers. For the pur-pose of clarity, we have decided to select the users that are responsible for the core of the process. The 43 users as shown take care of 15 out of 17 activities. The activities not considered in this initial analysis are070 PVand170 Parkeer. The former of the two is only performed by project leaders, whose only respon-sibility in the project is to approve project-related invoices. This is not a major part of their regular work, while the inclusion of these dozens of project leaders would make the diagram unreadable. The latter activity,170 Parkeer, is not a real activity, as we explained in the previous section.
Some SNA diagnostics look at the social network as a whole while others focus on a single node (i.e., performer). For example, if all other individuals are
3 Note that, throughout the paper, the real user names are changed into anonymous
in short distance to a given node and all geodesic paths (i.e., the shorted paths in the graph between two nodes) visit this node, clearly the node is very central - like a spider in the web. There are different metrics for this intuitive notion of centrality, such as betweenness, in and out closeness, and power [11] of each node. Table 3 shows both the top and bottom ranked performers based on the betweenness metric (note that the bottom performers in the table all share the same value). Betweenness expresses the extent to which a node lies between all other pairs of nodes on their geodesic paths. Formally, for a nodei:
betweenness(i) = g X j=1, k=1 GP AT HSj→i→k GP AT HSj→k
wheregis the size of the network,GP AT HSj→k is the total number of geodesic paths from node j to node k and GP AT HSj→i→k is the number of geodesic paths from nodej to nodekinvolving node i. In other words, the betweenness value for a node becomes higher when it is visited more often on a shortest path between two other points.
Table 3.Performers having high and low values for betweenness when analyzing the social network shown in Figure 7.
ranking name betweenness
1 user1 0.152 2 user4 0.141 3 user23 0.085 4 user5 0.079 5 user16 0.065 6 user13 0.057 7 user18 0.052 8 user2 0.049 9 user7 0.04 .. .. .. 35 user9 0 36 user20 0 37 user22 0 38 user30 0 39 user35 0 40 user36 0 41 user39 0 42 user41 0 43 user42 0
From the responses of the RWS process owners to this analysis, we learned that, typically, performers with high scores (e.g. user1 and user4 in Table 3) work for the administrative department in supportive functions. This confirms a
general insight that highly connected people often are assistants. Because the ad-ministrative department is responsible for both the preparation and completion of the handling of each invoice, its staff is involved in the handling of each case, giving them strong ties with other performers. The managers of RWS indicated, however, that not all of the people in these positions were present in the top of the lists, indicating that having a supportive function is not sufficient in itself to become highly connected.
The performers with bottom scores could be categorized as follows. First of all, project leaders were highly represented in the bottom of the lists (e.g., user9). They play an isolated role in the handling of invoices, normally they are not involved in any steps other than approving invoices related to their own projects. Second, performers with limited formal verification responsibilities could identified as well (e.g.,user22).
The second category of relatively unconnected performers could be traced back to auxiliary logins (e.g.,user30), used by system administrators and man-agement to deal with exceptional circumstances. An example of an exception is an invoice that is being withdrawn while its processing has already started. The isolated ‘participation’ of this category of users is therefore not very surprising. It did, however, make the managers conscious of the visibility of this type of ir-regular interference. One manager remarked: “So, auditors can derive this type of information too...”.
The third category turned out to be more surprising, as it involved senior positions in the contractual and financial departments (e.g., user41). At least nominally, they are expected to be actively involved in the process. Their low po-sition could indicate that a large amount of work being executed with a workflow management system is delegated to their juniors. Also, one of these performers was about to retire in a couple of weeks, explaining her current low centrality.
So far we only looked at sociograms based on the transfer of work metric (i.e., the frequency of passing work from one performer to another). However, we can also use thesubcontractingmetric which is related to the transfer of work metric. The main idea behind the subcontracting metric is to count the number of times individualj executed an activity in-between two activities executed by individual i. In a sense, there is a loop of work between such individuals. This may indicate that work was subcontracted from i to j. To find subcontracting relationships between people, we analyzed the occasions where a direct succession of contractor and subcontractor takes place with only one event in between the steps performed by the contractor. Figure 8 shows the resulting social network. In this network, the direction of arcs is important. The start node of an arc represents a contractor, while the end node of an arc represents a subcontractor. The displayed network has 43 nodes and 146 links. It also contains 8 nodes that are isolated from the network, which means they are not involved in a direct subcontracting relation.
Triggered by the many loops uncovered in the process mining analysis (see Section 6) and the social network based on the subcontracting metric, the RWS process owners were interested to learn more about the places in the process
Fig. 8.Social network based on subcontracting metric.
where work seemed to circle. After some discussion, they selected four places in the process where going “back-and-forth” is particularly undesirable. The four loops of interest, which can all be clearly distinguished in Figure 6, are as follows:
– 030 1e Vastlegging → 050 Adm akkoord → 030 1e Vastlegging – 050 Adm akkoord → 080 Contract akkoord → 050 Adm akkoord – 050 Adm akkoord → 070 PV → 050 Adm akkoord
– 080 Contract akkoord → 070 PV → 080 Contract akkoord
Each occurrence of this pattern is highly undesirable, as it slows down the processing of the invoice without any progress being made. Note that from an organizational perspective, it is just as undesirable when the work package is routed back to the original contractor as to a colleague with a similar organiza-tional role.
In extending our analysis, it turned out that in the handling of over 17% of all invoices, at least once an undesired subcontracting takes place at either of the four identified places in the process. The exact distribution is shown in Figure 9. As can be seen, there are cases where 10 or more erroneous routings take place. As this figure seemed rather excessive, we carried out a further analysis.
As it turned out, from the 4,592 cases of undesired loops/subcontracting it happened 2,087 times (45%) that the routing back was initiated by the workflow management system without any human performer initiating this action. This could be seen in the RWS log, since no human performer of an activity was
0% 2% 4% 6% 8% 10% 12% Percentage of total invoices 1 2 3 4 5 6 7 8 9 >10
Number of undesired subcontracting occurences within the handling of a single invoice
Fig. 9.Distribution of undesired loops within the handling of invoices.
registered, but instead a time-out message appeared. The workflow management system had been configured in such a way that whenever a step becomes ready to be executed because a preceding step is completed, it starts keeping track of the time the case is idle. When this time exceeds a time period of two weeks, the system passes the case back to the last performer being active on the case. In the extreme case of the loop050 Adm akkoord → 070 PV → 050 Adm akkoord, this active involvement of the workflow management system was responsible for 57% of the iterations. So, at least in part, the loops can be explained by an overly long passivity of performers in combination with the particular configuration of the workflow management system to act on that automatically. It is interesting to note here that the performers responsible for070 PVare the project leaders, who normally operate outside the main RWS building where the invoice processing takes place. As we mentioned before, the approval of invoices amounts to only a very small part of their work, which is focused on the execution of infrastructural projects.
Based on these insights, the RWS management took steps to improve the process. Before we discuss these steps, we will first show the additional analyses from a case perspective in the following section. As will be shown, the case per-spective helped to develop an even better understanding of the looping behavior.
8
Mining the Case Perspective
In this section we will illustrate business process mining from the third and last perspective: the case perspective. In other words, the emphasis is on the “what” question in the handling of invoices. Focusing on the case perspective is most interesting when different properties of individual cases are available. Some properties may refer to the activities being executed and the performers working on a specific case. Other properties are directly linked to a case (i.e.,
independent of the way the case has been processed in the workflow management system), such as, for instance, the amount of money of an invoice. However, there are possibly interesting correlations between the practical processing of a case and properties directly linked to case. As the mining of data already has a long tradition, standard knowledge discovering techniques can be used to search for this kind of relations.
Information that could be directly distilled from the RWS log relates to the timeliness of payments now that a workflow management system is operational (see Table 4). The third column reports the invoice payment results as registered in the RWS log. The other information has been already presented in Table 2, but is repeated for clarity.
Table 4.The time until invoices are paid (i.e., norms and actual performance before and after the implementation of a workflow management system).
payment duration (days) Norm before WfMS after WfMS
0−31 90% 70% 84%
32−62 5% 22% 12%
63− 5% 8% 4%
As can be seen, the implementation of the workflow management system has accelerated the payment of invoices, but the norms are still not met. This triggers the following question: “Is there is a relation between the time that an invoice is being payed and the amount of money being involved with the invoice?”. Suppose that the payment of invoices with a small amount of money are the ones that are delayed the most, perhaps because they are considered as less important. If this is the case, the negative financial effects would not be so bad for the financial position of the RWS office. After all, the interest is proportional to the invoice sum. To address this specific question, the SPSS tool Answer Tree was used, resulting in 5 categories (automatically generated by Answer Tree) with values as reported in Table 5.
Table 5.The time of payment related to the amount of money.
time of payment overall ≤24 >24≤172 >172≤1618>1618≤8377 >8377
0−31 84% 86% 87% 86% 80% 76%
32−62 12% 10% 11% 11% 15% 15%
63− 4% 3% 2% 3% 5% 9%
#n 14043 1403 2810 5619 2807 1404
Unfortunately for the RWS office in question, it appears that specifically invoices with a high payment sum - the ones above 1,618 K euro - are delayed
more stronglythan others. It follows from the table that 20% of the cases between 1,618 and 8,377 K euro are paid too late and this is the case for even 24% of the cases above 8,377 K euro. For the categories with lower payment sums (below 1,618 K euro), overly late payments are between 13% and 14%. An explanation for this phenomenon may be that people are reluctant to accept the responsibility for approving an invoice involving a high sum of money. Be it as it may, this clearly illustrates the kind of insight that can be gained from a case analysis. It also once more emphasizes the need to improve the invoice handling process.
Here, we present the further steps taken in the case analysis of the RWS log that follow up on the results of the analyses as presented before. As we identified from the mining of the process control flow, various loops are a significant part of the main process, as many cases follow these paths (see Section 6). Furthermore, we took a closer look at the performers responsible for the various steps involved in these loops, in particular considering four specific parts in the process (see Section 7).
By taking a case perspective, in particular by combining the information on the execution history of specific cases and their corresponding processing times, we try to gain a deeper understanding of the impact of these loops. We will once more focus on the loop behavior involving the four activities 030 1e Vastlegging, 050 Adm akkoord, 070 PV, and 080 Contract akkoord. Note that the loops prioritized by the RWS process owners themselves are com-posed of precisely these activities. Again, we used Answer Tree with the three payment classes (A: ≤ 31, B: ≥ 32 and ≤ 62, C: ≥ 63) as the target classifi-cation on the one hand and the number of times a case visits one of the four activities as predictors on the other. All other Answer Tree parameters are set to default values. Below, we discuss the information in the automatically generated decision tree.
First of all, activity 050 Adm akkoord appears the best predictor for the payment classification (see Table 6). This is the activity where the registration takes place of the invoice’s payment sum, the relevant budgets for paying it, and the required distribution over these budgets. The actual verification whether the payment should be done needs then yet to take place. Normally, such a verification is either done by a project leader in the case that the payment involves a specific project or by a clerk if the invoice falls within the conditions of a long-running contract.
Table 6. The time of payment distribution related to the number of times activity
050 Adm akkoordis processed.
time of payment overall 0 1 2 >2
0−31 84% 99% 91% 77% 36%
32−62 12% 1% 8% 18% 40%
63− 4% 0% 1% 5% 24%
From Table 6 it clearly follows that when activity050 Adm akkoord is exe-cuted twice for a particular case, the probability of the involved payment being delayed beyond the 31 day limit is much higher (23%) than when this activity is not executed at all (1%) or just once (9%). If the activity050 Adm akkoordis pro-cessed more than two times, the probability of a payment being too late even in-creases to 64%. Note that considering the four places of undesired subcontracting identified earlier, the repeated execution of activity050 Adm akkoordmay indi-cate occurrences of either the loop050 Adm akkoord → 080 Contract akkoord → 050 Adm akkoordor 050 Adm akkoord → 070 PV → 050 Adm akkoord.
It is now interesting to find out whether it is activity080 Contract akkoord or activity 070 PV that is most related to late payments. These are the two alternatives for a loop involving050 Adm akkoord, being the two principal ways that the actual verification of the invoice can take place. By examining the lower leaves of the decision tree, we found that it is in fact activity 070 PVthat best explains these delays in the case that050 Adm akkoord is processed more than two times. It is exactly this activity that needs to be executed by the project leaders, who often work remotely from the RWS main office. If the activity 070 PVis processed more than two times, the probability of the payment being too late increases to 75%.
The analysis presented in this section clearly identifies how multiple execu-tions of specific activities contribute to the late processing of invoices. In response to this insight, the RWS process owners informed the project leaders responsi-ble for executing 070 PV on how their purported passivity affected the overall performance of the payment process. Recall that the analysis from the organi-zational perspective already indicated that the workflow management system often acted by itself to hand back a case to a previous performer, because the intended performer of an activity did not respond in time. As it turned out, the project leaders were not aware of the impact of their actions. They agreed to give the invoice approval a higher priority in their work load, even though it is not a core part of their daily work. This nicely illustrates the phenomenon that performers often do not have a good insight into the wider context of a business process [20].
9
Reflection and Conclusions
In this paper, we presented a case study illustrating the practical application of process mining. To present the case study we used three perspectives: the process, organization, and case perspective. As a starting point, we used an industrial event log extracted from an operational workflow management system. This proprietary workflow management system is being used (amongst others) to support the process of handling invoices at a local RWS office in the Netherlands. We described how one can use a mixture of standard and specific mining tools to carry out this analysis.
The most important outcome from a process mining perspective was the dis-covery of themain flow in the invoice handling process. Because the generated
process model incorporated information on the execution frequencies of activi-ties, it could clearly be seen that loops in this process are far from exceptional. Inspired by this observation, process mining from an organizational perspective focused on the subcontracting metric in particular, leading to the identification of places in the process where the circling of work is undesirable. By inspecting these places more closely, the specific roles of two kinds of process performers were identified: the workflow management system itself and the project lead-ers. Additional mining from the case perspective revealed that the various loops indeed have a great impact on the process performance. It confirmed that the activity that needs to be executed by project leaders, who often work remotely, is strongly affecting the performance of the process in terms of timeliness.
Based on this industrial application of process mining, we can make three important observations. First of all, the practical application of business process mining is already feasible using the techniques embedded in the ProM frame-work. Nonetheless, it seems necessary to take realistic characteristics of event logs into account, such as e.g. the existence of noise. The successful application of our heuristics-based mining approach illustrates this point. Second, business process mining is a promising and potentially effective way to deal with real organizational challenges. The results from our analyses enabled the involved management to formulate and target specific organizational measures. The min-ing results could be used as an objective support for these measures. As a side effect, process mining made the management aware of the visibility of irregu-lar behavior. Finally, the case study showed that it is worthwhile to combine different mining perspectives to reach a richer understanding of the process. In this case, for example, the control flows revealed the various loops, but it took an organizational perspective to identify the key players, and a case-oriented analysis to understand the impact of these loops on the process performance.
In performing this business process mining project, we learned animportant lesson. It seems crucial to be closely involved with the people of the organization itself to carry out a meaningful analysis. As a small illustration of this point, it would have been impossible to determine the real value of the oddly connected activity 170 Parkeeron the basis of the event log itself (i.e., without direct in-volvement of the process owner). This activity turned out not to be an activity at all, but rather a workflow management system facility to suspend an opera-tion. More importantly, it took the input of the RWS process owners to identify and prioritize four locations of the process that seemed of interest to subject to a closer analysis. This certainly helped to speed up the identification of relevant results.
Furthermore, the case study also showed that process mining also has a num-ber of obviouslimitations. First of all, we can only monitor the events that are actually logged. This implies that some interactions may not be visible. More-over, people may find ways to work around the system. Second, the system may enforce certain interaction patterns. If workers are completely controlled by the system, the discovered process models and sociograms reflect the system rather than the organization. Fortunately, most systems offer a lot of flexibility when
it comes to the selection and ordering of work-items. Even workflow manage-ment systems allow for a pull mechanism where workers select work-items from a shared pool in any order. Moreover, other types of process-aware information systems (e.g., ERP, CRM, PDM systems) tend to allow for even more flexibility. Finally, it is clear that there are privacy issues that may complicate the appli-cation of process mining (in particular the organizational perspective). It would have been relatively easy on the basis of the logged information to determine which specific individual performers are responsible for the slow processing of invoices. Although this might be important information to optimize the process further, we did not carry out this analysis. Privacy issues, labor contracts, and other agreements are crucial to determine to what level practical mining analyses may proceed.
To conclude this paper, we briefly discuss our plans for future work. We are currently extending and improving our mining techniques and continue to do so given the many challenges and open problems. For example, we are developing genetic algorithms to improve the mining of noisy logs. Recently, we added a lot of new functionality to the ProM framework.4 Therefore, our first priority is to apply process mining in a wide variety of practical situations. Interesting application domains that we would like to target (and where we already have some limited experiences) are:
– Health care
The flow of patients in a hospital and other health-care related processes could be discovered using process mining. Just measuring performance in-dicators such as flow times and frequencies does not provide enough insight in the actual process. Effective health-care management requires more fine-grained information that is truly process-oriented. Hence process mining could really help to improve processes here.
– Web services
Cross-organizational workflows are enabled by web-services technology, but also require an agreement on a common process. However, one party cannot force the other party to work in a certain way. Therefore, one can make agreements and check these or try to discover the behavior of the other parties involved. Process mining can be used to discover the “choreography” of web-services and detect deviations. This is a way to operationalize the notion of abstract processes in BPEL [9] and choreographies in [25]. – Case handling
Case handling systems such as FLOWer [34] extend existing workflow tech-nology with more flexibility. As a result, users can deviate from the “normal” flow of work. Process mining can be used to discover and quantify these de-viations. ProM is already able to mine from FLOWer logs and we applied this in a Dutch social security agency. However, we would also like to apply our tools to the change logs of systems such as ADEPT [37].
4 See www.processmining.org for more information and to download the tools used in
Related to the application of the ProM framework in real-life situations is the transfer of ideas to commercial tools. In our collaboration with IDS Scheer some of our results have been incorporated in ARIS PPM. We hope that more software vendors will adopt more of the ideas presented in this paper.
Acknowledgements
The authors would like to thank Andriy Anikolov, Laura Maruster, Anne Rozi-nat, Christian G¨unther, Huub de Beer, Monique Jansen-Vullers, Michael Rose-mann, and Peter van den Brand for their on-going work on process mining techniques and tools at Eindhoven University of Technology. Minseok Song vis-ited Eindhoven University of Technology with funding by the BK21 program. He would like to thank the Ministry of Education of Korea for its financial support through the BK21 program.
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